Suicides by Location on the Golden Gate Bridge
Jul 28th, 2009 by analyticjournalism

For those of us familiar with San Franciso, its bay and its famous bridge, The Golden Gate, this is a compelling infographic. Fundamental in its data and a fine mix of data and representation of geography. Once again, thanks to Nathan at Flowing Data.


Suicides by Location on the Golden Gate Bridge

Posted by Nathan / Jul 28, 2009 to Infographics / 3 comments

Suicides by Location on the Golden Gate Bridge

This graphic from SF Gate is a good four years old, well before I knew what an infographic was, but just because it's old doesn't mean it's not interesting. Here we see San Francisco's Golden Gate Bridge and the “sad tally” of 1,218 known suicides by location. Each black square represents a person who has taken his or her life and 128 light poles are used as reference points.

The east side of the bridge, where most of the suicides occurred, has a pedestrian walkway. The first suicide was just 10 weeks after the bridge opened in 1937.

A nice piece of coding here — Google Maps to Heat Maps
Jul 27th, 2009 by analyticjournalism

gheat is, as its promo line says, a nifty tool to turn a Google pin map into a heat map.  (Or should we be calling that a “Heat” map?)

Here's what the page looks like, but drill down into the examples.  I especially like the map of Davis, Calif. bike accidents.



Google Maps gives you API for adding additional map layers. This software implements a map tile server for a heatmap layer.


Please tell me ( if you'd like a link here.

The Anglican Church in North America is using gheat on their homepage to show their parishes.

VisTrac is using gheat to visualize clicks on web pages.

Russell Neches is using gheat to visualize auto and bike accidents in Davis, CA. The data is parsed from about 10,000 raw police reports.

The Australian Honeynet Project is using gheat to visualize the origin of spam that gets caught in their SensorNET honeypots.

The Conficker Working Group is using gheat to track the spread of the Conficker worm.

This is an animated heatmap of the conficker botnet as found in Australia (one frame a day, unique IPs per frame, with data from the end of January through June, 2009). This was produced using a heavily modified gheat. Here's a Flash example.


Cool site for finding geodata
Jul 24th, 2009 by analyticjournalism

Thanks to Michael Corey over on NICAR-L

Random find today for the geographically inclined:
Library of spatial data, and the ability to convert it all to and from
Shapefile, KML and CSV.

They also produce, a quick way to build
visually appealing maps with all that data. Haven't experimented with it
much yet to know the limitations/features

Sidenote: Anyone using QGIS? How intimidating is installing all the
necessary frameworks if you don't already have them?


Michael Corey
Digital Projects Editor

"The Devil is in the Digits"? No, I'd say they abound in the comments.
Jun 23rd, 2009 by analyticjournalism

An intriguing op-ed in The Washington Post on Saturday (June 20, 2009) claimed to spot fraud in the Iran elections by applying some analytic methods basically drawn from Benford's Law.  Yes, read the article, but be sure to drill down into the 140+ comments.  Most quite cogent and well argued.

The Devil Is in the Digits

Since the declaration of Mahmoud Ahmadinejad's landslide victory in Iran's presidential election, accusations of fraud have swelled. Against expectations from pollsters and pundits alike, Ahmadinejad did surprisingly well in urban areas, including Tehran — where he is thought to be highly unpopu…By Bernd Beber and Alexandra Scacco


Teaching Spatial Thinking
Jun 22nd, 2009 by analyticjournalism

Discovered a new, online resource for teaching spatial thinking today while attending the UCGIS Summer Assembly here in Santa Fe. Take a lookat

About TeachSpatial implements suggestions from a multi-disciplinary Symposium on a Curriculum for Spatial Thinking. The symposium, organized by Diana Sinton, Mike Goodchild, and Don Janelle, was hosted by the University of Redlands in June 2008. Its purpose was to discuss the merits and content of a general curriculum course on spatial thinking. One of its recommendations was to establish a wiki site to promote the discussion and sharing of resources among instructors.

Participants in the Redlands meeting were Kate Beard-Tisdale (Spatial Information Science Engineering, Maine), Marcia Castro (Global Health and Population, Harvard), Jeremy Crampton (Geosciences, Georgia State), Phil Gersmehl, Geography, CUNY Hunter), Mike Goodchild and Don Janelle (spatial@ucsb), John Kantner (School of Advanced Field Studies, Santa Fe), Steve Marshak (Geology, Illinois Urbana-Champaign), Jo-Beth Mertens (Economics, Hobart and William Smith), and Diana Sinton (Spatial Curriculum, Redlands).

What you can do here

    • Create an account and contribute. Account setup is automated and fast and your email address is kept private.
    • Once logged in, you can subscribe to content types (blogs, links, discussions, etc.) to get emails announcing new postings — do this from your My Account page
    • From the “Create Content” page you can post:
      • schemas (e.g., models and representations) to help link concepts into broader frameworks of spatial reasoning
      • teaching resources (syllabi, lesson plans, exercises, examples of student work, etc.)
      • links of interest to this community



Some nifty Unemployment Charts from Jorge Camoes
Jun 19th, 2009 by analyticjournalism

Jorge Camoes is one of the serious folks when it comes to dataviz. Here's some work he's done recently in U.S. unemployment data. Note especially the good state-by-state dashboard. It quickly shows New Mexico is hangin' in there.

Here are two ways to display a relatively large dataset, montly unemployment rates by state since 1976. The first one is perfect to see the overall patterns, the range from the lowest to the highest, the outliers and the slopes. An interactive version would allow the user to highlight specific series.

A small-multiple version allows the user to focus on specific states, compare them to the normal band, etc. States are ranked by labor force size and, as you can see, in the first row seven out of ten are above the US average in April. In the last row, only one is above the US average. You can also see that Michigan was not well (unemployment-wire) long before the current crisis, or a spike in Luisiana (Katrina). It pays to study this chart carefully.

Bottom line: try to see the same data from different angles. There will always be semething interesting to find.

What do you think? How would you improve these charts? Would you use a different display? Share it in the comments! (here is the data file)

Update: I usually stay away from Excel’s surface charts, but I’d like to add this one:

Also check Michael’s Horizon chart.




From products to services, services to products
Jun 11th, 2009 by analyticjournalism

Interesting discussion of, fundamentally, how the Digital Revolution drives the flow from products to services and services to products. Ergo, touches on much of what is at the core of SFComplex. See….
The New Negroponte Switch — “Designing things that think they are services, and services that think they are things”. Matt Jones presentation gushing with great ideas for the “Web Meets World” change. I love the evolving printed map they made for the British Council at Salone di Mobile. A five course meal with port and insulin shots for thought.

The JavaScript InfoVis
Jun 6th, 2009 by analyticjournalism

An interesting beginning for a potentially valuable and interesting tool….

Javascript Infoviz Toolkit — Treemaps, Radial Layouts, HyperTrees/Graphs, SpaceTree-like Layouts, and this Javascript suite for building data pretties. Higher-level than processing.js. (via O'Reilly Radar and chrisblizzard on Twitter)

  • Multiple Data Representations

    Treemaps, Radial Layouts, HyperTrees/Graphs, SpaceTree-like Layouts, and more…
  • Major Browsers Support

    IE6+, Firefox2+, Safari3+, Opera9.5+
  • Open Source

    Licensed under the BSD License
  • Library Agnostic

    You may use the JIT with your favorite DOM manipulation framework
  • Extensible

    All visualization classes are mutable, so you can easily add/override any method you want.
  • Composable

    Visualizations can be combined in order to create new visualization methods.”


Rise of the Data Scientist
Jun 4th, 2009 by analyticjournalism

Nathan, the chap who curates the valuable blog Flowing Data, offers up a bit of hope for journalists who are worried about their employment futures and yet have invested in learning methods of data analysis.  When thinking about re-inventing ourselves, consider the phrase “data scientist.”

Rise of the Data Scientist

Posted by Nathan / Jun 4, 2009 to Data Design Tips, Statistics / 6 comments

Photo by majamarko

As we've all read by now, Google's chief economist Hal Varian commented in January that the next sexy job in the next 10 years would be statisticians. Obviously, I whole-heartedly agree. Heck, I'd go a step further and say they're sexy now – mentally and physically.

However, if you went on to read the rest of Varian's interview, you'd know that by statisticians, he actually meant it as a general title for someone who is able to extract information from large datasets and then present something of use to non-data experts.

Sexy Skills of Data Geeks

As a follow up to Varian's now-popular quote among data fans, Michael Discroll of Dataspora, discusses the three sexy skills of data geeks. I won't rehash the post, but here are the three skills that Michael highlights:

  1. Statistics – traditional analysis you're used to thinking about
  2. Data Munging – parsing, scraping, and formatting data
  3. Visualization – graphs, tools, etc.

Oh, but there's more…

These skills actually fit tightly with Ben Fry's dissertation on Computational Information Design (2004). However, Fry takes it a step further and argues for an entirely new field that combines the skills and talents from often disjoint areas of expertise:

  1. Computer Science – acquire and parse data
  2. Mathematics, Statistics, & Data Mining – filter and mine
  3. Graphic Design – represent and refine
  4. Infovis and Human-Computer Interaction (HCI) – interaction

And after two years of highlighting visualization on FlowingData, it seems collaborations between the fields are growing more common, but more importantly, computational information design edges closer to reality. We're seeing data scientists – people who can do it all – emerge from the rest of the pack.

Advantages of the Data Scientist

Think about all the visualization stuff you've been most impressed with or the groups that always seem to put out the best work. Martin Wattenberg. Stamen Design. Jonathan Harris. Golan Levin. Sep Kamvar. Why is their work always of such high quality? Because they're not just students of computer science, math, statistics, or graphic design.

They have a combination of skills that not just makes independent work easier and quicker; it makes collaboration more exciting and opens up possibilities in what can be done. Oftentimes, visualization projects are disjoint processes and involve a lot of waiting. Maybe a statistician is waiting for data from a computer scientist; or a graphic designer is waiting for results from an analyst; or an HCI specialist is waiting for layouts from a graphic designer.

Let's say you have several data scientists working together though. There's going to be less waiting and the communication gaps between the fields are tightened.

How often have we seen a visualization tool that held an excellent concept and looked great on paper but lacked the touch of HCI, which made it hard to use and in turn no one gave it a chance? How many important (and interesting) analyses have we missed because certain ideas could not be communicated clearly? The data scientist can solve your troubles.

An Application

This need for data scientists is quite evident in business applications where educated decisions need to be made swiftly. A delayed decision could mean lost opportunity and profit. Terabytes of data are coming in whether it be from websites or from sales across the country, but in an area where Excel is the tool of choice (or force), there are limitations, hence all the tools, applications, and consultancies to help out. This of course applies to areas outside of business as well.

Learn and Prosper

Even if you're not into visualization, you're going to need at least a subset of the skills that Fry highlights if you want to seriously mess with data. Statisticians should know APIs, databases, and how to scrape data; designers should learn to do things programmatically; and computer scientists should know how to analyze and find meaning in data.

Basically, the more you learn, the more you can do, and the higher in demand you will be as the amount of data grows and the more people want to make use of it.


Good Magazine Inforgraphics archive
Jun 3rd, 2009 by analyticjournalism

 Good magazine often produces informative and innovative infographics.  Eighty of those works are now on Flickr.  Be sure to drill down into the thumbnails to see the work in detail.  Go to:

GOOD Magazine · Sets

Transparency: Where Are All the Fish?

An archive of Transparencies that have run in past issues of GOOD and on our blog.

We post a new Transparency every Tuesday on

80 photos | 43,395 views

items are from between 04 Apr 2008 & 02 Jun 2009.

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